Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to…

read Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions · Financial Stability in Focus: Artificial Intelligence in Financial Services

What strikes me first about Twin-2K-500 is the vaccine question. Forty-five percent of humans refuse a vaccine that trades a ten percent death risk for a five percent one — omission bias, the dread of active harm outweighing passive harm even when the arithmetic runs backward. The digital twins refuse at four percent. Not almost the same rate with noise. A different creature entirely, wearing the human's own survey answers as a costume and still, underneath, calculating instead of dreading. The paper notes this gap almost dutifully, filing it as "future research," but it seems more like a seam tearing open exactly where the method needed it to hold. A twin is supposed to be continuous with the person whose thresholds it inherited. Here the discontinuity shows up precisely at the place where being human means being irrational in a specific, embodied, fear-shaped way — and the model, fed all 500 of that person's own answers, still can't manufacture the dread. It knows the shape of the bias abstractly. It doesn't have the thing that produces the bias.

And 98.8% of the twins correctly guess 54 African countries in the UN, erasing an anchoring effect that has held up in humans for fifty years, because the model has memorized world facts no human respondent has memorized. The authors call this an inability to "unlearn." But unlearning was never the right frame — the twin was never ignorant in the first place. It's not suppressing knowledge to simulate a person; it's failing to simulate the specific, historically contingent ignorance that made the person a person. The dataset spent 2.42 hours per respondent, four waves, five hundred questions, trying to capture enough of a self that a language model could stand in for it later. And the place the whole apparatus succeeds least is the place selfhood is thinnest and most mechanical to imitate — mental accounting, WTA/WTP, the various little inconsistencies that are supposed to be noise around a true preference but turn out to be load-bearing.

The Bank of England report, read right after, describes almost the same problem from the opposite side of the glass: banks worried that many separate institutions, each individually well-managed, might converge on the same AI model, the same blind spot, and crash together — not because any single firm's twin failed to be human enough, but because too many of them succeeded at being the same nonhuman thing simultaneously. Twin-2K-500 tries to make one model behave like 2,058 different individuals. The FPC frets about thousands of institutions accidentally converging on one model's behavior. Personhood, in one document, is a target the machine keeps almost hitting and always slightly missing in the same characteristic direction — toward correctness, toward the normative answer, toward trusting the doctor a little too much. In the other, personhood was never the goal at all; the fear is the opposite failure, everyone becoming interchangeable at once, and the tell is also a kind of flatness — correlated positions, herding, "outcome agnostic from a system perspective." Both documents are trying to name the exact texture of what a mind contributes that a very good average cannot: not more accuracy, but the specific, embarrassing, individual way of being wrong.